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A Comprehensive Literature Review on Deep Learning–Driven Multilingual Chatbots for Low-Resource Languages with a Focus on Marathi–Hindi–English Interaction

by Bharti Borade, Charansing N. Kayte
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 77
Year of Publication: 2026
Authors: Bharti Borade, Charansing N. Kayte
10.5120/ijca2026926240

Bharti Borade, Charansing N. Kayte . A Comprehensive Literature Review on Deep Learning–Driven Multilingual Chatbots for Low-Resource Languages with a Focus on Marathi–Hindi–English Interaction. International Journal of Computer Applications. 187, 77 ( Jan 2026), 44-53. DOI=10.5120/ijca2026926240

@article{ 10.5120/ijca2026926240,
author = { Bharti Borade, Charansing N. Kayte },
title = { A Comprehensive Literature Review on Deep Learning–Driven Multilingual Chatbots for Low-Resource Languages with a Focus on Marathi–Hindi–English Interaction },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2026 },
volume = { 187 },
number = { 77 },
month = { Jan },
year = { 2026 },
issn = { 0975-8887 },
pages = { 44-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number77/a-comprehensive-literature-review-on-deep-learningdriven-multilingual-chatbots-for-low-resource-languages-with-a-focus-on-marathihindienglish-interaction/ },
doi = { 10.5120/ijca2026926240 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-02-01T00:33:39.954168+05:30
%A Bharti Borade
%A Charansing N. Kayte
%T A Comprehensive Literature Review on Deep Learning–Driven Multilingual Chatbots for Low-Resource Languages with a Focus on Marathi–Hindi–English Interaction
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 77
%P 44-53
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Conversational Artificial Intelligence (AI) has undergone substantial progress, evolving from rule-based systems to advanced transformer-driven multilingual models. However, research for low-resource Indian languages—particularly Marathi and Hindi—remains limited despite rapid technological advances. This review synthesizes studies from 2000 to 2025, covering rule-based chatbots, retrieval methods, Seq2Seq architectures, multilingual transformers, and self-supervised speech models such as wav2vec 2.0 and HuBERT. The analysis highlights key linguistic challenges, including agglutination, free word order, transliteration, regional accents, and pervasive code-mixing. Although models like mBERT, XLM-R, and MuRIL significantly improve multilingual understanding, they still struggle with hybrid inputs and domain-specific conversational tasks. Persistent gaps include limited datasets, weak ASR–NLU integration, and insufficient cultural grounding. The review outlines future directions for developing robust, culturally aligned Marathi–Hindi–English chatbots.

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Index Terms

Computer Science
Information Sciences

Keywords

Multilingual Chatbots; Marathi–Hindi–English NLP; Transformer Models; mBERT; XLM-R; MuRIL; Seq2Seq; wav2vec 2.0; HuBERT; Low-Resource Languages; Code-Mixing; Conversational AI; Speech Recognition; Natural Language Understanding (NLU); Deep Learning; Indian Languages

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